Location tracking sensors have proliferated and there has come to exist an abundance of data in the form of trajectories. Such datasets are rich in information and have consequently attracted much attention in disciplines relating to data analytics. Trajectory datasets have been mined and analyzed for applications such as cellular network optimization, emergency detection, and taxi-route suggestions. Generally, a trajectory dataset can be regarded as being indicative of paths taken by objects from a starting point to an ending point, wherein “object” can refer to any physical or other entity describing a “path” defined by essentially any physical or other parameter.
Generally, in analyzing trajectories, an anomaly is defined as an observation (or set of observations) that deviate(s) significantly from the rest of the data (e.g., with respect to a predetermined standard); thus, an anomalous trajectory or sub-trajectory represents a trajectory or sub-trajectory that so deviates. This can amount to pinpointing one or more smaller trajectories that qualify as anomalies in the context of one or more larger trajectories. Challenges continue to be encountered in efficiently finding and designating such anomalies, and viable, cost-effective solutions continue to elude.